
where \(A \in \mathbb {R}^{m \times n}\)  is the discretization of a linear operator, \(x^{gt} \in \mathbb {R}^{n}\)  and \(y \in \mathbb {R}^{m}\)  arise in various applications of signal or image processing, such as deblur, super-resolution or tomographic reconstruction [1]}, [2]}.
It is well-know that such inverse problems are ill-posed and their solution is very challenging when noise affects the data.
We consider here the case of data corrupted by Gaussian noise, i.e.
\(y^\delta = Ax^{gt} + e \qquad e \sim \mathcal {N}(0, \delta ^2 I)\) 
